Wireless sensor networks generally include a large number of nodes with sensing, computing, and wireless communication capabilities. These networks can be used in a variety of industries, such as health care, pollution monitoring, and target tracking systems. To prolong the network lifetime and efficiently manage a WSN, the nodes in the network are generally formed into clusters. One node in each cluster is then selected as the cluster head. The cluster head can be selected randomly or based on certain criteria. A selection based on predetermined criteria, however, can significantly lengthen the lifetime of a network. As a result, many different clustering methods have been developed in recent years.
Some of these clustering methods select cluster heads without giving regards to the amount of energy needed and used by each node. These methods employ a probability function to select cluster heads, which often does not require a lot of energy for the clustering process. However, these clustering approaches do not choose the appropriate cluster head to maximize the network lifetime. In contrast, some cluster head selection methods take into account the nodes' energy to select the best cluster heads. However, a large number of messages need to be exchanged between various nodes during such clustering approaches.
Some of the recently developed cluster head selection algorithms are distributed. These methods generally require partial knowledge of environmental conditions. On the other hand, some methods need global knowledge of network nodes to create the best clusters. These methods are, in general, centralized. The centralized approaches, however, are not applicable to large-scale WSNs, because gathering all information for each node is a time and energy-consuming task.
Therefore, a need exists for a more flexible and energy-efficient cluster head selection method in wireless sensor networks.